Abstract: This work provides an experimental comparison between classical PID, analytically compensated PID, and fuzzy control applied to the speed control of a rover actuator based on a permanent magnet DC motor. Unlike most studies, which focus on classical metrics such as transient response and steady-state error, this work incorporates kinematic indicators such as acceleration and jerk to characterize the dynamic effort applied to the actuator. The results indicate that the fuzzy controller achieves the fastest transient response and the best disturbance rejection, although at the cost of an IAJ 2.378 times higher than that of the classical PID and a peak jerk 79.36% higher under nominal conditions. The classical PID exhibits the smoothest kinematic profile under nominal operation, but under disturbances it generates jerk peaks 2.39 times higher than the fuzzy controller and an IAJ 1.67 times higher than the compensated PID, evidencing its inadequacy under variable loads. The compensated PID achieves the lowest cumulative IAJ under disturbance, outperforming the fuzzy controller by 6.7%, and provides the best overall balance between response speed, disturbance rejection, and cumulative mechanical wear.

Abstract: This manuscript develops a timelike-boundary reading of locality and reality within the established Lorentzian causal structure of special relativity and the standard record language of quantum measurement. The central object is a timelike boundary equipped with a boundary observer field and observer-adapted cuts. Such a cut is treated as the local comparison surface on which selected quantities are read relative to a coarse-grained reference structure. A local record appears when a boundary-relative deviation becomes resolvable on that cut. The framework separates two roles that are often compressed into one event statement. Lorentzian geometry supplies causal admissibility: it determines which prior data or contextual contributions may be relevant for a candidate event. The boundary comparison supplies record content: it identifies the deviation that becomes locally manifest. Thus the causal cone constrains the admissible domain, but it does not by itself provide a microscopic route or a measurement record. The proposed reading therefore assigns locality to cut-local record formation under Lorentzian causal admissibility. Reality is associated with stable, record-accessible deviations rather than with direct exposure of the underlying reference structure. The result is a compact assignment framework in which causal structure, reference structure, resolved deviation, and local record formation are organized on the same timelike boundary without replacing the established mathematical content of special relativity or quantum mechanics.

Abstract: The topological properties of planetary fluids are typically analyzed by mapping classical fluidequations onto complex quantum mechanical models. Here we present a purely real, six-dimensional Stueckelberg quantum mechanical formulation of the rotating shallow water equations to demonstrate that these topological features are intrinsic to the classical kinematics itself. Operating entirely within R^6, we decouple the complex quantum geometric tensor into an independent, real Fubini-Study metric and a real antisymmetric Berry curvature. Our real-variable approach explicitly derives a topological magnetic monopole of charge C=2 and captures the inherentscale invariance of the fluid's geometry without the need for complexification. We suggest that continuous variations in the Coriolis parameter may dynamically model the deep-time planetary evolution of the Archean Earth, and we propose a laboratory rotating-tank experiment to physically measure this topological phase transition. Finally, we show that our real 6D formulation naturally maps to unbroken supersymmetric quantum mechanics. By identifying a purely real supercharge and calculating a fluid Witten index of W = -2, we advance a mathematically supported viewpoint that steady-state geostrophic weather patterns represent the unbroken, zero-energy supersymmetric ground states of the rotating fluid system. Consequently, the topological isolation of this vacuum naturally restricts the spectral flow across the equator, providing a theoretical explanation for the unidirectional eastward motion of equatorial boundary waves.

Abstract: Credit risk assessment requires both accurate prediction and structured decomposition of how hetero-geneous evidence contributes to each decision. Monolithic Large Language Models can incorporate unstructured evidence and natural-language reasoning into such workflows, but in high-stakes un- derwriting they may be distracted by noisy inputs, miss rare but decisive risk cues, and offer limited control over policy-dependent decision thresholds. We present CREDITAGENT, a hierarchical credit review system with three stages: evidence filtering, specialist risk analysis by agents, and decision fusion. Our central contribution is holding the adapted backbone, specialist-agent outputs, hard-stop rules, and data split fixed, we vary only the final fusion strategy to isolate the effect of hierarchical fusion on underwriting quality. On a held-out set of 6,000 personal credit cases from Chinese financial institution, CREDITAGENT achieves 83.32% accuracy and a Business Efficiency Coefficient of 0.7647 outperform flagship model. We present these findings as an institution-specific case study while identifying which components (hierarchical fusion, GRPO training recipe) are mechanism-portable versus institution-specific (hard-stop rules, cost ratios). To ensure reproducibility, we make code and dataset publicly available at https://github.com/kouzhizhuo/Credit_Agents.

Abstract: The functioning of complex natural structures, such as living systems, still lacks a generally accepted theoretical basis with respective empirical experimental verification for decades. We propose a class of experiments to test whether such systems could be subject to an unknown ordering principle that cannot be captured by known physical laws. We hypothesise that the quantum mechanical uncertainty principle enables ordering phenomena in nearly chaotic systems in the sense of a strong emergence principle, which would not be expected when they are modelled conventionally, as several authors have already formulated in various forms. To account for the harsh conditions prevailing in living systems that may preclude fragile macroscopic quantum coherence, our hypothesis does not require such coherence at all, contrary to earlier related proposals. To test this hypothesis, two virtually identical and sufficiently complex experimental setups should be compared. One setup will operate with deterministic pseudo-random number generators at key sensitive points, while the other one will use quantum-based physical random-number generators, the two setups being otherwise identical. Existing artificial neural networks are proposed as possible test objects, and their performance under identical training conditions can be used as a quantitative benchmark. As this working hypothesis extends far beyond artificial networks, a successful outcome of such an experiment could have significant implications for many other branches of science.

Abstract: Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by a decline in functional capacity and increasing limitations in daily activities. Clinical assessment tools provide valuable information on symptom severity but do not fully capture functional capacity. The aim of this study was to determine the usefulness of the Senior Fitness Test (SFT) for assessing functional capacity in patients with PD, depending on met (group A) or did not meet (group B) health-promoting physical activity (PA) recommendations. The study included 74 patients with idiopathic PD classified as Hoehn and Yahr stage II. PA was assessed using ActiGraph GT3X+ triaxial accelerometer and activity diaries, functional capacity using SFT, and symptom severity by MDS-UPDRS scale. Group A achieved significantly better results in all SFT components and had lower MDS-UPDRS scores than group B. The synthetic functional index was higher in the A group (1.28 ± 2.25 vs. -0.64 ± 2.28; p < 0.001), whereas the total MDS-UPDRS score was lower (31.09 ± 4.97 vs. 34.99 ± 5.28; p = 0.022). The results indicate that the SFT may be a useful and practical tool to complement the clinical assessment of patients with PD and may support more individualized rehabilitation planning and monitoring.

Abstract: Non-invasive methods for monitoring the condition of laboratory animals play a key role in ensuring animal welfare and improving the reliability of scientific data. This study evaluates the effectiveness of two non-invasive approaches - daily body weight measurement and urine analysis by qPCR - for monitoring the health of BALB/c mice infected with tick-borne encephalitis virus (TBEV). Calculation of the first derivative of body weight change allowed precise determination of disease onset, which correlated with clinical symptoms and detection of viral RNA in urine. Mathematical analysis of body weight change dynamics (first derivative with type 2 cubic spline smoothing, rh = 1) showed that a derivative threshold value of ≤ −0.6 reliably distinguishes infected BALB/c mice from healthy ones (AUC = 1 in ROC analysis). Urine analysis by qPCR allowed for the detection of viral RNA as early as the second day after infection, with a peak on the seventh day. The mathematical model was further tested on C57BL/6, CBA, and BALB/c mice of different ages and proved to be suitable. The threshold values of the derivative were found to be dependent on the mouse strain. The proposed non-invasive methods offer a humane and accurate alternative to invasive procedures, contributing to higher ethical standards and quality of research in virology.

Abstract: Water hyacinth (Eichhornia crassipes) is an invasive aquatic plant with high lignocellulosic content, offering potential as a natural fiber resource for craft-based industries. However, its extremely high initial moisture content (≈95%) presents a major challenge in fiber processing, particularly for small-scale industries that rely on traditional sun-drying methods. These methods are highly dependent on weather conditions, prone to contamination, and produce inconsistent fiber quality. This study adopts a research and development (R&D) approach to design and evaluate an innovative dryer machine specifically for water hyacinth fiber processing. The proposed system utilizes LPG-based heating and controlled airflow to achieve stable drying conditions. Experimental results show that the dryer machine can process 10 kg of wet water hyacinth within 280 minutes, significantly shorter than approximately four days required for manual drying. The system reduces the moisture content to below 10%, resulting in improved fiber cleanliness, uniformity, and usability. Although the dried mass produced by the machine is slightly lower compared to manual drying, this is attributed to more effective moisture removal, leading to lower residual water content in the final product. Productivity analysis indicates improved operational consistency and higher processing capacity over extended periods (30–180 days), particularly under varying weather conditions. These findings demonstrate that controlled drying technology provides a reliable and efficient solution for lignocellulosic fiber processing in small-scale industries, contributing to improved material utilization and sustainable biomass management.

Abstract: Soil health and irrigation water quality are fundamental to sustainable agricultural productivity, particularly in semi-arid environments. This study evaluated the influence of irrigation water quality on soil physical and chemical properties within the Kagitumba Irrigation Scheme in Eastern Rwanda. An observational analytical design integrated field sampling, laboratory analysis, and statistical evaluation. Soil samples (n = 20) were col-lected at depths of 0–30 cm and 30–60 cm, alongside irrigation water samples (n = 5) from intake and distribution points. Soil parameters analyzed included texture, bulk density, pH, electrical conductivity (EC), organic matter, and nutrient content, while water quality assessment focused on pH, EC, turbidity, dissolved oxygen (DO), and oxidation–reduction potential (ORP). Data were subjected to descriptive statistics, Pearson correlation, and ANOVA at a 95% confidence level. Findings revealed predominantly sandy loam soils with low bulk density, moderate water-holding capacity, and near-neutral pH. Soil salin-ity remained low, indicating limited risk of degradation. Irrigation water was generally suitable for agricultural use in terms of pH and salinity; however, elevated turbidity showed a strong negative correlation with infiltration rate (r = −0.73). Additionally, low soil nitrogen levels were significantly associated with water quality, suggesting nutrient leaching. These results underscore the critical role of irrigation water quality in shaping soil health and emphasize the need for improved water filtration and integrated nutrient management to enhance long-term sustainability.

Abstract: The complete blood count (CBC) is a diagnostic test to analyze abnormalities of blood cells. Currently, automated hematology analyzers and artificial intelligence technology are being used with automated blood analyzers to ensure accuracy and reliability. This study aimed to evaluate the performance of artificial intelligence (AI) based automated blood cell analyzer, Awalife AI-100Vet Multifunctional Morphological Analyzer, in dog and cat blood samples by comparison with the CBC manual method. In dogs, PCV, hemoglobin, RBC, MCH, WBC, % Neutrophil, %Lymphocyte, %Monocyte, %Eosinophil and %Reticulocyte were all significantly correlated. While in cats, PCV, Hemoglobin RBC, WBC, % Neutrophil, % Lymphocyte, and % Eosinophil were all also significantly correlated. AUC values obtained by the Awalife AI-100Vet analyzer for Hematology testing in dogs and cats were 0.72 and 0.92 respectively. These findings suggest that the Awalife AI-100Vet analyzer demonstrated good accuracy using dog blood for hematology testing as well as excellent accuracy when using cat blood. The AI-based automated blood analyzer has the potential to analyze hematological data and is close to the reference method. However, there are still differences in some parameters. Further optimization of the AI algorithm, which will involve increasing the accuracy of identifying unusual cell shapes, improving stability against various samples, such as stains, and achieving good results when working with unique pathologies, should be carried out.

Abstract: In vitro-transcribed modified mRNA is a promising platform for transient protein replacement in regenerative medicine, including cardiomyocyte regeneration, but repeated dosing is limited by variable innate immune activation and ISR-mediated translation shutdown across individuals. We propose a Human-specific PRR/ISR Immunogenicity Atlas: a focused, genotype-aware computational framework linking patient genetic variation in pattern recognition receptors and ISR components to predict immune and translation responses for IVT modRNA. The Atlas generates individualized “genetic passports” that stratify responder risk, estimate cytokine and PKR/eIF2α activation, and prioritize clinically feasible temporary knockdown strategies (LNP-siRNA/ASO or small molecules). We outline a six-stage roadmap covering data integration, feature engineering, multi-modal modeling, uncertainty quantification, a knockdown prioritization module, and open deployment. Ethical, privacy, ancestry-representation, and regulatory considerations are discussed, along with a staged validation strategy. This Atlas provides a conceptual and practical framework to support safer, more consistent protein replacement in regenerative medicine by moving from one-size-fits-all to genotype-guided approaches.

Abstract:

Dixit et al. proposed an asymptotic drag scaling for zero-pressure-gradient flat-plate turbulent boundary layers based on the approximation $M\sim U_{\tau}^2\delta$, where $M$ is the kinematic momentum rate through the boundary layer, $U_{\tau}$ is the friction velocity, and $\delta$ is the boundary-layer thickness. In the present paper, an explicit Reynolds-number-dependent correction to this approximation is derived from the logarithmic mean-velocity profile. Integration of the log law across the layer yields $M\sim U_{\tau}^2\delta\,f(Re_{\tau})$, where $Re_{\tau}=\delta U_{\tau}/\nu$ is the friction Reynolds number and $f(Re_{\tau})$ is given analytically. Application of the correction to the dataset compiled by Dixit et al. shows that the corrected scaling gives an exponent consistent with the asymptotic value $-1/2$ within bootstrap confidence intervals, whereas the uncorrected formulation does not. The correction should be viewed as a leading-order amendment, since the derivation uses the logarithmic law outside its strict range of validity.

Abstract:

Background: Streptococcus agalactiae (group B Streptococcus, GBS) remains a leading cause of invasive infections in pregnant women, fetuses, and neonates. Universal screening at 36-37 weeks of gestation followed by intrapartum antibiotic prophylaxis is essential to prevent adverse outcomes. However, data on GBS serotype distribution are limited in several regions, including Greece. This study aimed to determine the prevalence, serotype distribution, and antimicrobial susceptibility of GBS isolates among pregnant women in Greece. Methods: Vaginal and rectal swabs were collected from pregnant women undergoing routine GBS screening between January 2021 and December 2025. Samples were processed using selective enrichment broth and cultured on blood agar and chromogenic media. Identification was based on standard microbiological methods, CAMP test, and VITEK2 system. Antimicrobial susceptibility testing and macrolide-lincosamide-streptogramin B (MLS_B) phenotyping were performed. Serotyping was conducted using a commercial latex agglutination assay. Results: Among 941 women screened, 118 (12.5%) were colonized with GBS. The most prevalent serotypes were III (29.7%), V (18.6%), Ib (14.4%), IX (10.2%), Ia (9.3%), and II (9.3%). All isolates were susceptible to penicillin. Resistance to erythromycin and clindamycin was observed in 29.7% and 22.9% of isolates, respectively. The predominant MLSB phenotype was constitutive (cMLSB, 78.4%), followed by inducible (iMLSB, 13.5%), L (5.4%), and M (2.7%) phenotypes. Conclusions: GBS colonization was detected in 12.5% of pregnant women, with serotype III predominating, underscoring its clinical relevance due to its association with invasive neonatal disease. Although penicillin remains fully effective, the observed resistance to macrolides and lincosamides, primarily mediated by the cMLS_B phenotype, raises concerns regarding alternative therapies.

Abstract: Federal post-quantum cryptography migration is scoped around three categories of cryptographic assets: libraries, protocols, and key stores. We argue that this scoping is incomplete. Cryptographic functions and key material can be realized in the parameters of machine learning models, and current open-source serialization-focused scanners we evaluated do not detect them. We provide an existence proof: a 30-layer feed-forward ReLU network that realizes AES-128 exactly, with the master key and all eleven round keys resident directly in layer bias vectors and recoverable by parsing. The construction validates bit-exactly against FIPS 197 and the NIST CAVP AESAVS known-answer subsets across 10⁴ random plaintext-key pairs, including under float32 quantization. We then argue analytically that ML-KEM and ML-DSA private keys hide more comfortably in modern weight tensors than AES keys do, not less, by virtue of their larger size and lower internal rigidity. The consequence under the harvest-now-decrypt-later threat model is that any long-lived cryptographic key embedded in an open-weights model artifact distributed today is recoverable by any future party with knowledge of the embedding scheme, without any quantum capability required. We propose a parameter-space cryptographic recognizer operating on structural, parametric, and functional signatures, integrated with cryptographic bill-of-materials tooling as a parameter_resident_cryptographic_content emission class extending the MBOM-PQC schema. The audit primitive is defense-in-depth: it closes the gap for known constructions and architectural fingerprints without claiming completeness against adaptive adversaries. We make no claim that any deployed model contains such an embedding; the contribution is the existence of the capability, the absence of detection, and the migration-scope consequence.

Abstract: A robust evaluation of predictive uncertainty is essential for deploying machine learning models in high-risk sectors. While various techniques such as Gaussian Processes and Bayesian Neural Networks have been developed to address model uncertainty, the measurement uncertainty associated with input data, particularly regarding heteroscedasticity and autocorrelation, is often overlooked. This work introduces the Generalized Least Squares Support Vector Machines (GLS-SVM), a kernel-based regression model designed to integrate the full variance-covariance matrix of the response variable into the training process. A GUM-consistent methodology is then developed for evaluating prediction uncertainty including a correction for model bias. The performance of the proposed model is validated using two case studies: a simulated regression problem with heteroscedastic, autocorrelated noise and the calibration of a mass flow controller. Results demonstrate that GLS-SVM significantly outperforms other kernel-based models when dealing with correlated data, providing accurate estimations and physically consistent standard uncertainties. This approach offers a versatile, metrologically-informed framework for data-driven regression tasks where measurement covariance information is available and rigorous uncertainty quantification is required.

Abstract: Background: Type 2 diabetes mellitus (T2DM) and periodontitis are chronic inflammatory conditions. Periodontitis may amplify low-grade systemic inflammation in people with T2DM. High-sensitivity C-reactive protein (hsCRP) reflects this inflammatory burden, but the effect of non-surgical periodontal therapy (NSPT) on hsCRP in T2DM remains uncertain. Objective: To evaluate whether NSPT changes hsCRP at 3 and 6 months compared with oral hygiene instructions alone in patients with T2DM and periodontitis. Methods: Predefined secondary analysis of a 1:1 parallel-group randomized trial with assessments at baseline, 3 months, and 6 months. Participants received scaling and root planing plus oral hygiene instructions (intervention) or oral hygiene instructions only (control). Fasting hsCRP (mg/L) was analyzed on the log scale using mixed-effects models; effects are presented as exponentiated ratios with 95% confidence intervals. Sensitivity analyses included baseline-adjusted analysis of covariance (ANCOVA) and covariate-adjusted mixed models. An exploratory group-adjusted regression examined associations between periodontal changes and hsCRP change. Results: Eighty-nine participants were randomized (45 control, 44 intervention), with hsCRP available for most participants through 6 months. There was no between-group difference at 3 months (ratio 0.958; 95% CI 0.875–1.049; p=0.358). At 6 months, hsCRP was lower in the NSPT group than in controls (ratio 0.809; 95% CI 0.738–0.887; p<0.001), corresponding to ~19% lower hsCRP; the model-based geometric mean hsCRP at 6 months was 2.66 mg/L versus 3.26 mg/L. Periodontal measures improved more with NSPT, but changes in periodontal measures were not independently associated with hsCRP change after group adjustment. Conclusions: In patients with T2DM and periodontitis, NSPT was associated with lower hsCRP at 6 months, suggesting a potential systemic anti-inflammatory benefit. These findings support periodontal care as part of integrated management in T2DM.

Abstract: Swelling soil landslides pose severe challenges in geotechnical engineering due to non-linear deformation and strength degradation. Accurate characterisation of pore structure parameters remains the core difficulty. This study proposes a Physics-Informed Neural Network (PINNs) framework that utilises Mercury Intrusion Porosimetry (MIP) data to simultaneously invert three key physical parameters: pore fractal dimension (Ds), surface tension (γ), and contact angle (θ). By embedding the Washburn equation and fractal pore theory into the neural network loss function, the framework achieves high-precision inversion without requiring complete prior information. Validated on three expansive soil samples, the inverted Ds values were 2.47, 2.53, and 2.58, showing excellent agreement with classical models (R² > 0.99) and an average relative error below 2.3%. The inverted γ ranged from 0.476 to 0.480 N/m and θ from 142.3° to 144.2°, both satisfying physical plausibility requirements. Five-fold cross-validation confirmed the absence of overfitting (ΔR² < 0.001). Sensitivity analysis identified Ds as the dominant parameter controlling pore volume distribution; Ds exceeding 2.55 indicates elevated landslide susceptibility. This framework provides a rapid, automated approach for extracting pore structure parameters, offering parametric support for preliminary risk assessment of expansive soil slopes.

Abstract: This work explores how graph theory concepts and neural networks can assist in strategic planning of metro network expansions using publicly available city data with the São Paulo Metropolitan Region. The methodology consolidated information from multiple public sources, developed a formula to estimate passenger demand based on catchment areas, applied Random Forest to identify the most relevant demographic features, and implemented a GraphSAGE model for demand prediction, extracting predictive capability from the topology of the system as well as socioeconomic features and Origin-Destination trips. The model achieved an R² of 0.874 ± 0.042 with minimal overfitting, outperforming the Random Forest approach. It can be applied to predict demand for future projects in a way that is accurate, inexpensive and computationally efficient, while also not requiring any rail system specific information besides topology. In this project, it was used to analyze multiple real projects and proposals for the São Paulo Metropolitan Region. Analysis revealed that employment, residences, and destinations where people go to eat represent 65% of the predictive capacity in the city.

Abstract: This study aimed to identify the volatile organic compounds (VOCs) present in the es-sential oil (EO) of Schinus lentiscifolia and to evaluate the effect of chitosan coatings (1%) enriched with EO of S. lentiscifolia (1000, 2000, and 4000 mg L⁻¹) on the control of Penicillium sp. and on the quality of ‘Fuji’ apples. The EO was extracted from S. lentiscifolia collected in the municipality of Lages, Santa Catarina State, Brazil, in March, May, and November 2022. The antifungal activity of S. lentiscifolia EO against Penicil-lium sp. was evaluated in vitro. Apples were stored under refrigerated conditions (0 ± 0.5 °C; 90 ± 5% RH) for 30 days and subsequently under ambient conditions (23 ± 3 °C; 70 ± 5% RH) for 6 days. A total of 14 VOCs were identified in the EO of S. lentiscifolia, with the monoterpenes β-pinene (34.68%) and α-pinene (30.61%) as the major com-pounds, followed by β-terpinene (10.13%), camphene (9.66%), and o-cymene (7.14%). The application of chitosan coating with S. lentiscifolia EO (2000 mg L⁻¹) reduced the severity of blue mold in ‘Fuji’ apples by 88.1% during refrigerated storage and by 69.2% under ambient conditions. Ethylene production by the apples was also reduced when treated with chitosan and EO. No influence of the treatments was observed on fruit quality attributes. The postharvest application of chitosan coatings combined with S. lentiscifolia EO reduces disease caused by Penicillium sp. in ‘Fuji’ apples without affect-ing fruit quality.

Abstract: Discoidin domain receptor 1 (DDR1) has been implicated in fibrotic progression in multiple organs, including the kidney; however, its role in regulating cytoskeletal organization and matrix remodeling in renal fibroblasts remains unclear. Here, we investigated how DDR1 expression is regulated by profibrotic stimulation and extracellular matrix stiffness, and how DDR1 influences cytoskeletal organization and collagen remodeling. Single-cell RNA sequencing of murine kidneys subjected to unilateral ureteral obstruction (UUO) revealed enrichment of Ddr1 expression in transitional fibroblast populations during early activation. In vitro, transforming growth factor-β1 (TGF-β1) increased DDR1 expression, but DDR1 depletion did not affect canonical myofibroblast marker expression. Instead, DDR1 depletion suppressed stress fiber assembly while promoting actin-rich podosome formation associated with matrix degradation. Functionally, DDR1-deficient cells exhibited impaired focal adhesion maturation, enhanced collagen degradation, reduced gel contraction, and decreased collagen matrix stiffness as measured by atomic force microscopy. Furthermore, extracellular matrix stiffness dynamically regulated DDR1 expression, suggesting a bidirectional relationship between DDR1 expression and matrix mechanics. Together, these findings identify DDR1 as a modulator of cytoskeletal remodeling that governs the balance between matrix-degradative and contractile remodeling programs in renal fibroblasts.